Offering Interactive Technology Without Screen Time Is Cognitoys Dino

If you are a mom like me, you love for your child to be able to entertain themselves at times
WITHOUT
having to stick a phone or tablet in their hands. The
7-inch tall dinosaur
offered in green, blue or pink will quickly become your child’s best friend. While including a very personal interaction, CognitoysDino will play music, tell stories, crack jokes, help your child process their feelings, teach
AND ENCOURAGE THEM!

WITHOUT
7-inch tall dinosaur
AND ENCOURAGE THEM!

Once set up is complete, Cognitoys Dino will remember your child’s name and birthday. Also, Dino will remember all of their favorite things from the many conversations your child will have with their new friend. With just one simple button, the dino is extremely easy for children to operate all on their own. It grows with the child in content as it tries to maintain age appropriate responses.

If my little girl had a choice, she would stay up all night playing with Dino. Parental control panels allowparents to set bedtime and wake up times. This allows the dinosaur to be inactive during times when your child should be asleep.

Giving my daughter as many learning tools possible is very important to me. Not only does Cognitoys provide my child with opportunities to learn subjects like math and language but it also teaches her it is ok to feel emotions both good and bad. One of my favorite things about Dino is, if my daughter is feeling sad, she can express that. When expressing her feelings, Dino responds by telling her he is sorry she feels sad and that it will be ok but yet he encourages her to talk to an adult about it. Cognitoys Dino has been a great outlet in helping teach my daughter that emotions are both important and normal. Above all the benefits Cognitoys Dino has to offer, I am most pleased with the emotional piece.

Consider the gift of a “friend” this Christmas with Cognitoys Dino and you too will see your child blossom!

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Functional MRI first-level analyses were done using SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK;
http://www.fil.ion.ucl.ac.uk
). All functional images were first (i) realigned; (ii) slice time corrected to the first slice; (iii) coregistered to a structural MRI image; (iv) normalized to MNI space using unified segmentation; and (v) spatially smoothed using an 8 mm Gaussian kernel. We used several analyses to verify that head movements did not differ between sessions (
Supplementary Table 1
).

Muscle activity of the most-affected forearm (wrist flexors and extensors) was measured using magnetic resonance-compatible EMG (Brain Products; sampling frequency = 5000 Hz) during scanning in all 15 patients. We used Brainvision Analyzer 2.0 (Brain Products, Germany) for preprocessing and FieldTrip (
Oostenveld
et al.
, 2011
) for time-frequency analyses. We performed the same analysis as described in our previous study (
Helmich
et al.
, 2011
). Preprocessing included (i) magnetic resonance artefact correction; (ii) downsampling to 1000 Hz; (iii) filtering with a 20–200 Hz bandpass filter to remove movement artefacts; and (iv) rectifying the signal to enhance the information on EMG burst-frequency (tremor) of the signal. Subsequently, we calculated the time–frequency representations between 1–20 Hz in steps of 0.1 s using a 2-s Hanning taper, resulting in a 0.5 Hz resolution. By averaging over all time points we obtained an average power spectrum across segments. For each patient, we calculated the time course of EMG power at each subject’s individual tremor frequency. The average tremor frequency across subjects was not significantly different between sessions (OFF: 4.6 ± 0.2 Hz; ON: 4.5 ± 0.1 Hz; mean ± SEM). This resulted in patient-specific regressors describing fluctuations in tremor amplitude (EMG-amp). To remove outliers, we calculated the logarithmic values of the EMG-amp and z-normalized the data within subjects. To capture activity related to changes in tremor amplitude, we calculated the first temporal derivative of the EMG-amp regressor (EMG-change). Importantly, there were no significant differences in EMG variance [as determined by the coefficient of variation (COV)] between both sessions [EMG-amp, OFF COV = 67.1, ON COV = 35.1 (
P
= 0.77; two-tailed
t
-test); EMG-change, OFF COV = −32.2, ON COV = −45.8 (
P
= 0.92; two-tailed
t
-test)].

After convolution of both EMG regressors with a haemodynamic response function, we considered EMG-amp and EMG-change as explanatory variables in a multiple regression analysis. The first-level general linear model (GLM) also included separate regressors of no interest: average signal across the whole brain (global signal to correct for head motion;
Power
et al.
, 2014
), in the bilateral ventricles, and over a blank portion of the magnetic resonance images (Out-of-Brain signal). Furthermore, we included 36 regressors describing head motion based on linear, quadratic and cubic effects of the six movement parameters belonging to each volume as well as the first and second derivative of each of those regressors (to control for spin-history effects) (
Lund
et al.
, 2005
). Each GLM contained both sessions. Parameter estimates for all regressors were then obtained by maximum likelihood estimation.